Traditionally, a universal background model (UBM) is used to analyze acoustic signals for speaker recognition. The UBM outputs numerical acoustic indices that do not correspond to the phonetic or lexical content of the input speech signal. Speech content and the distortions it produces in the acoustic signal have been largely ignored in prior work on text-independent speaker verification.
A deep neural network (DNN) is a feed-forward neural network that is both much larger (e.g., a few thousand nodes per hidden layer) and much deeper (e.g., 5-7 hidden layers) than traditional neural networks.
Moreover, prior approaches to performing combined speech content and speaker recognition usually involved applying different and separate analyses to the received speech information, either in parallel or series. This is because the models used for each type of analysis focus on different aspects of the speech signal. Such approaches, however, can be overly time or processing resource consuming, thereby limiting real time determinations or requiring the speech data to be transferred to remote server devices for processing. Such approaches limit the scope of applications for speech analysis systems and are also highly language dependent.
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